Model checking of emergent behaviour properties of robot swarms

Juurik, S; Vain, Jüri

doi:10.3176/proc.2011.1.05

Cited by 4 publications

(2 citation statements)

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“…In model checking all the possible behaviours of a system are exhaustively explored to determine whether the requirements are satisfied or violated. Model checking has previously been employed to verify robotic swarms [6,7,8,13,15].…”

Section: Introductionmentioning

confidence: 99%

Symmetry Reduction Enables Model Checking of More Complex Emergent Behaviours of Swarm Navigation Algorithms

Antuña

Araiza-Illan

Campos

et al. 2015

Towards Autonomous Robotic Systems

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The emergent global behaviours of robotic swarms are important for them to achieve their navigation task goals. These emergent behaviours can be verified to assess their correctness, through techniques like model checking. Model checking exhaustively explores all possible behaviours, based on a discrete model of the system, such as a swarm in a grid. A common problem in model checking is the state-space explosion that arises when the states of the model are numerous. We propose a novel implementation of symmetry reduction, in the form of encoding navigation algorithms relatively with respect to a reference, exploiting the symmetrical properties of swarms in grids. We applied the relative encoding to a swarm navigation algorithm, Alpha, modelled for the NuSMV model checker. A comparison of the state-space and verification results with an absolute (or global) and a relative encoding of the Alpha algorithm highlights the advantages of our approach, allowing model checking both larger grid sizes and higher numbers of robots, and consequently verifying more complex emergent behaviours. For example, a property was verified for a grid with 3 robots and a maximum allowed size of 8 × 8 cells in a global encoding, whereas this size was increased to 16 × 16 using a relative encoding. Also, the time to verify a property for a swarm of 3 robots in a 6 × 6 grid was reduced from almost 10 hours to only 7 minutes. Our approach is transferable to other swarm navigation algorithms.

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Section: Introductionmentioning

confidence: 99%

Symmetry Reduction Enables Model Checking of More Complex Emergent Behaviours of Swarm Navigation Algorithms

Antuña

Araiza-Illan

Campos

et al. 2015

Towards Autonomous Robotic Systems

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“…The corresponding process consists of modelling, specification, and verification. Examples of the use of model checking are [15], which describes the use of model checking for verifying the control system of an unmanned aircraft, [11], which deals with the application and extension of model checking techniques for verifying spacecraft control software, and [10], which explains how model checking can be applied to verify a distributed coordination algorithm for robot swarms.…”

Section: Related Workmentioning

confidence: 99%

Verification of Behaviour Networks Using Finite-State Automata

Armbrust

Kiekbusch

Ropertz

et al. 2012

Lecture Notes in Computer Science

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Abstract. This paper addresses the problem of verifying properties of behaviour-based systems used for controlling robots. A behaviour-based system typically consists of many interconnected components, the behaviours, which in combination realise the system's overall functionality. The connections between the behaviours are crucial for the correct operation of a system. Therefore, key properties of behaviour-based systems are verifiable based on their behaviour interconnections. In this paper, it is described how behaviour-based networks can be (automatically) modelled using finite-state machines and how model checking with the Uppaal toolbox can then be applied for verification and analysis tasks.

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